Method for optimising a mixture of components by means of spectral analysis

ABSTRACT

The invention relates to a method for optimizing a mixture of components for the production of a target product by means of spectral analysis, preferably by means of spectral analysis (topological) in the near infrared region (NIR). The aim of the method is to produce a target product by batch mixing and/or continuous mixing of the “n” components thereof, on the basis of different flows of said components with controlled concentrations and/or flow rates, said target product requiring a set of ranges of values of physiocohemical characteristics for the commercialization thereof. According to the method, a batch mixer or a continuous mixer is supplied with said components with controlled concentrations and/or flow rates.

This invention relates to a method and an optimization device of amixture of components to obtain a target product by spectral analysis,preferably by near infrared (“NIR”) (topological) spectral analysis.

In particular, this invention relates to the regulation of mixtures ofthe components by batch or on line, such as for example, petroleumproduct mixtures or others, in which said mixtures are in accordancewith a set of significant specifications.

More in particular, this invention relates to a method and anoptimization device of a mixture of components for obtaining a targetproduct by spectral analysis, for example by NMR, Raman, IR, and/orUV/Visible, preferably by near infrared (“NIR”) (topological) spectralanalysis, under constraint, said constraint being based

-   -   on the preferred use of at least one of the components of the        mixture, and/or    -   on the modification of at least one characteristic of the target        product.

When a mixture of various components, the objective is to obtain atarget product with a range of values having certain physical-chemicalcharacteristics. By way of example, for a mixture of petroleum products,for example, a fuel, these characteristics may be its octane rating, itscetane index, its resistance to cold, the polyaromatic compound content,the vapor pressure, etc. . . . .

The present invention will apply in particular to the mixing ofcomponents of target petroleum products (for example fuels) in anysuitable place, for example a refinery, an oil depot and/or any deviceusing a petroleum product consisting of a mixture of components preparedby batch and/or online.

Thus, in a petroleum depot and/or an oil refinery, motor fuels can beproduced by the so-called batch and/or in-line mixing technique, inwhich the various liquid components and any additives are introduced ina tank and/or a line serving as a mixer.

This introduction of the various components may be carried outsimultaneously or not. This mixture of various components may beperformed by batch and/or continuously. The flow of the variouscomponents and/or the concentration of the various components aregenerally ordered and controlled by a computer and the preparation timeof a fuel lot may take several dozens of hours.

It is to this type of industrial plant that reference will be made moreprecisely in the remainder of this description, without this implying alimitation of the scope of application of the invention. The propertiesof the manufactured target product (for example, a fuel) are repeatedlychecked during manufacture and analyzes are carried out for this purposeon samples taken from the mixer and/or in the storage enclosure duringfilling. From the results of these analyzes, the flow rates and/or theconcentrations of the components of the mixture as well as any additivesare adjusted to align the measured values with the set values. For eachanalysis, it is advantageous to minimize the time interval between thetaking of a sample of the mixture being prepared and obtaining themeasured value; although “on-line” analyzers can theoretically meet thisneed, it has been found that rapid changes in the components and thecharacteristics of the target products do not always make it possible tobe fully effective and that it was still often necessary to sample andcarry out the analyzes “off-line”.

Indeed, a particularity of the components mixed for the preparation ofthe target products is that they can either originate from differentsupply sources and/or simply vary in terms of quality and/orphysico-chemical characteristics over time. This feature has become allthe more critical in recent years because of the effects ofglobalization and the multiplicity of access to new supply sources.Therefore, there is a need in the art for having an improved means ofpreparing petroleum products which more effectively meets these newrequirements.

Another characteristic of the target petroleum products has also emergedin recent years. Environmental changes have led to the adoption of newstandards and/or permanent changes in certain characteristics of thetarget petroleum products. An example may be mentioned of the temporaryproduction of less polluting fuels during pollution peaks and/or thecontent of the bio-components of said fuels.

Therefore, there is a need in the art for having an improved means ofpreparing petroleum products which more effectively meets these newrequirements.

It is these problems that this invention proposes to solve by using amethod and/or device for optimization of a mixture of components forobtaining a target product by spectral analysis, for example by NMR,Ratnan, IR, NIR and/or UV/Visible, preferably by near infrared (“NIR”)(topological) spectral analysis.

In particular, this invention relates to the regulation of mixtures ofthe constituents by batch and/or on line, such as for example, petroleumproduct mixtures, in which said mixtures are in accordance with a set ofsignificant specifications.

More particularly, this invention relates to a method and a device foroptimization of a mixture of components for obtaining a target productby spectral analysis, for example by NMR, Raman, IR, NIR and/orUV/Visible, preferably by near-infrared (“NIR”) (topological) spectralanalysis under constraint, said constraint being based

-   -   on the preferred use of at least one of the components of the        mixture, and/or    -   modifying at least one characteristic of the target product.

Process M

Thus, this invention relates to a process M for preparation of a targetproduct, for example a target petroleum product, for example a fuel, bybatch and/or in-line mixing of its “n” components from different flowsfrom said components at controlled concentrations and/or flow rates,with optional incorporation of additives, said target product having tobe marketed with a set of ranges of physico-chemical characteristicvalues, method in which a batch and/or continuous mixer is fed saidcomponents at controlled concentrations and/or flow rates, said processbeing characterized in that:

-   1. there is at least one spectral datum characterizing the target    product and which defines its spectral range,-   2. spectral datum (data) is (are) available, each individually    characterizing at least two—preferably all the “n”—components of the    target product,-   3. a computer program is used which makes it possible to calculate    the ranges of respective proportions of said components necessary    for reconstituting a spectral datum of the mixture belonging to the    spectral range of Step 1 from the spectral datum (data) of Step 2,    and-   4. the ranges of respective proportions of the constituents of step    3 are used to control the concentrations and/or flow rates of the    components fed into the mixer so as to prepare the target product.

The spectral data are preferably data measured by the same type ofspectral analysis, preferably using the same type of spectrometer, thesespectral data may for example be “spectra”.

METHOD M1—Spectrum

Thus, this invention relates to a method M1 for preparation of a targetproduct, for example a target petroleum product, for example a fuel, bybatch and/or in-line mixing of its “n” components from different flowsfrom said components at controlled concentrations and/or flow rates,with optional incorporation of additives, said target product having tobe marketed with a set of ranges of physico-chemical characteristicvalues, method in which a batch and/or continuous mixer is fed saidcomponents at controlled concentrations and/or flow rates, this methodbeing characterized in that:

-   1. there is at least one analysis spectrum characterizing the target    product and which defines its spectral range.-   2. the analysis spectra is available, each individually    characterizing at least two—preferably all the “n”—components of the    target product,-   3. a computer program is used which makes it possible to calculate    the ranges of respective proportions of said components necessary    for reconstituting from a mixing spectrum belonging to the spectral    range of Step 1 from the spectra of the components of Step 2, and-   4. the ranges of the respective proportions of the components of    Step 3 are used to control the concentrations and/or flow rates of    the components feeding the mixer so as to prepare the target product

The spectral range characterizing the target product can be determinedby any suitable method. By way of example, this field will be determinedby means of the aggregates as described hereinafter in the description.The fundamental characteristic of the spectral range is that it definesthe fact that the final mixture conforms to a set of significantspecifications of the target product; this is what makes it possible tosay that the mixture is on spec (“on-spec” in the English language)according to the jargon used by a person skilled in the art.

The target product (e.g., a petroleum product) generally contains amajor component whose concentration by weight in the target product isthe highest. According to a particular and preferred embodiment of thisinvention, at least one of the spectra characterizing one of the minorcomponents (that is to say any non-major components) of the targetproduct of the aforementioned Step 2 is a spectrum obtained by analyzinga mixture of said minor component with either said major component or arepresentative mixture of the target product.

According to one embodiment of this invention, the number of components“n” of the target product is greater than or equal to two, for examplegreater than or equal to three.

As already indicated, the spectral data are preferably data measured bymeans of the same type of spectral analysis, preferably using the sametype of spectrometer, these spectral data may for example be anyappropriate type of spectral quantities constituting a correspondingspectral database. These spectral quantities may be any type of signalscharacterizing the spectra, for example the absorbances, transmittances,reflectances, etc. . . . , the optical absorbances or densities beingthe most commonly used signals. By way of example, we may also mentionas signals the derivatives of the absorbances or any other measurementresulting from another type of mathematical processing of saidabsorbances.

Method M2

Thus, according to a particular embodiment, the present invention alsorelates to a method M2 for preparing a target product, for example apetroleum target product, for example a fuel, by batch and/or onlinemixing of its “n” components from different flows of said components inconcentrations and/or controlled flow rates, with an optionalincorporation of additives, said target product requiring a set ofranges of values of physico-chemical characteristics to be marketed,method in which a batch and/or continuous mixer is fed with saidcomponents at controlled concentrations and/or flow rates, said methodbeing characterized in that:

-   1. there is a spectral database characterizing the target product    and defining its spectral range,-   2. a spectral database is available, each individually    characterizing at least two—preferably all the “n”—components of the    target product,-   3. A computer program is used to identify, in the spectral database    created in step 2, the mixtures of the components (and therefore    their respective ranges of proportions) whose spectrum is calculated    in step 2 from the spectral databases belonging to the spectral    domain of step 1,-   4. the ranges of respective proportions of the constituents of step    3 are used to control the concentrations and/or flow rates of the    components fed into the mixer so as to prepare the target product.

According to one particular embodiment of this invention, after thesecond step, from the spectral database of the components, a 2a spectraldatabase is formed characterizing the mixtures of said components andwhich is used for Step 3.

Method M2a

Thus, according to one particular embodiment, this invention alsorelates to a method M2a for preparation of a target product, for examplea target petroleum product, for example a fuel, by batch and/or in-linemixing of its “n” components from different flows from said componentsat controlled concentrations and/or flow rates, with optionalincorporation of additives, said target product having to be marketedwith a set of ranges of physico-chemical characteristic values, methodin which a batch and/or continuous mixer is fed said components atcontrolled concentrations and/or flow rates, this method beingcharacterized in that:

1. there is a spectral database characterizing the target product anddefining its spectral range,

2. a spectral database is available, each individually characterizing atleast two—preferably all the “n”—components of the target product,

2a. from the spectral databases of the Step 2 components, a spectraldatabase (2a) which characterize the mixtures of these components isformed,

3. a computer program is used to identify in the spectral databasecreated in step 2a (and thus the respective ranges of proportions) whosespectrum calculated from the spectral databases of step 2a belonging tothe spectral domain of Step 1,

4. the ranges of the respective proportions of the components of Step 3are used to control the concentrations and/or flow rates of thecomponents fed into the mixer so as to prepare the target product.

As described above, the spectral range characterizing the target productmay be determined by any appropriate method. By way of example, thisfield will be determined by means of the aggregates as describedhereinafter in the description. The fundamental characteristic of thespectral domain is that it defines the fact that the final mixtureconforms to a set of significant specifications of the target product;this is what makes it possible to say that the mixture is on spec(“on-spec” in the English language) according to the jargon used by aperson skilled in the art.

As already described above, the target product (e.g., a petroleumproduct) generally contains a major component whose concentration byweight is the highest in the target product. According to one particularand preferred embodiment of this invention, at least one of the spectraldatabases characterizing one of the minor components of the targetproduct of the aforementioned Step 2 is a spectral database obtained byanalysis of a mixture of said minor component (i.e., any non-majorcomponent) with either said major component or a representative mixtureof the target product.

According to one preferred embodiment of this invention, the spectralanalysis method used is chosen from, for example, NMR, Raman, IR, NIRand/or UV/Visible, preferably by topological spectral analysis in thenear infrared (“NIR”).

According to one preferred embodiment of this invention, the preparationmethod of a target product for on-line mixture of its components whichalso make it possible to take into account a set of new constraints andthus to optimize said method.

“X” Constraint

In one particular embodiment of this invention, when we wish to focus onthe use of the “X” component from among the “n” components of the targetproduct mixture, the method of preparation will include an additionalstep consisting of selecting from said respective ranges of theproportions of the components being fed into the mixer, the range ofproportion having the highest concentration would be that of the “X”component. The constraint of use of the “X” component may be preferred(or not) for various reasons, among which are those mentioned forillustrative purposes as economic and/or periodic cleaning reasons forthe “X” component storage container and/or reasons for improvedlogistical management linked to procurement.

Thus, in a particular embodiment, this invention also relates to the useof the aforementioned processes/methods for the preparation of a targetproduct comprising an “X” component, the use of which consists offocusing on the use of said “X” component selected from among the “n”components of the target product mixture, the method of preparationincludes an additional step of selecting from the ranges of respectiveproportions of the components fed to the mixer, the range of proportionhaving the highest concentration would be that of the “X” component.

Replacement “X′”

In another particular embodiment, this invention also consists in animproved selection method for choosing the supply of component “X′” toreplace component “X”, characterized in that this method comprises thefollowing steps

-   -   a. a spectral analysis step of component “X′” to determine a        characterizing spectral datum thereof, for example a spectrum        and/or a characterizing spectral database.    -   b. a second step consisting of a step to replace the spectral        datum characterizing component “X” with the spectral datum        characterizing component “X′” in Process M,    -   c. steps corresponding to Steps 1 and 3 of Process M,    -   d. step 3 of Process M of the preceding steps making it possible        to predict the rate of potential use of component “X′” in the        target product.

This method of improved selection of the choice of procuring component“X′” to replace component “X” is particularly revolutionary in that itis no longer based solely on economic considerations.

Indeed, the spectral analysis of the new component “X′” will enable theskilled person in the art to consider a multitude of factors among whichwe cite as examples, environmental factors, supply problems, etc. . . ..

Illustratively, the above mentioned second step may consist of either aspectral replacement step of component “X” with the spectrum of thecomponent “X′” in the Process M1, or a replacement step from thespectral database of component “X” with the spectral database ofcomponent “X′” in Process M2.

Thus, in one particular embodiment, this invention also relates to theuse of the aforementioned processes/methods for the preparation of atarget product

-   -   a. to validate the replacement of component “X” by component        “X′”,    -   and/or    -   b. predicting the potential use rate of component “X′”        -   during preparation of a target product initially comprising            component “X”, the preparation process including    -   a. a step to perform the spectral analysis of component “X′” to        determine a characterizing spectral datum, for example a        spectrum and/or a characterizing spectral database.    -   b. a step consisting of a step to replace the spectral datum        characterizing component “X” with the spectral datum        characterizing component “X′” in the Process.    -   c. a step to validate the replacement of component “X” with        component “X′” and/or to predict the rate of potential use of        component “X′” which makes it possible to verify that there is        at least one range of respective proportions of the new        components necessary for reconstitution of a spectral datum of        the mixture belonging to the spectral domain of the target        product.

According to one preferred embodiment of this invention, the spectralanalysis method used is chosen from, for example, NMR, Raman, IR, NIRand/or UV/Visible, preferably by topological spectral analysis in thenear infrared (“NIR”).

According to one preferred embodiment of this invention, the targetproduct is a fuel. Among the components of fuels, we may cite by way ofillustration, diesels, oxygenated gas oils, gasolines, oxygenates (forexample, BOB for “blend stock for oxygenate bending”), fatty acid esters(for example FAME) esters of vegetable oil (e.g., ethyl esters and/ormethyl esters), methyl tert-butyl ether (MTBE), tert-Amyl methyl ether(TAME), ethyl-Tert-butyl ether (ETBE), hydrogenated or partiallyhydrogenated vegetable oils (“HVO”), ethanol bioethanols, methanol, etc.These fuels may also contain any kind of optional additives, includingpro-cetane and/or pro-octane and/or pro-heptane, friction modifiers,detergents, antioxidants, cold-strength improvers, combustion improvers,anti-corrosive agents and/or mixtures thereof.

Thus, according to one embodiment of this invention, the mixture ofcomponents of target products can be made in any suitable place; by wayof example, we may mention any industrial complex comprising componentmixing operations for the preparation of a target product for example arefinery, a complex (petro-chemical, petroleum depot and/or any deviceusing a product made up of a mixture of components prepared by batchand/or in-line. This invention applies more particularly to terminals orany post-refinery mixing installation, preferably a mixture of fuels.

Topological spectral analysis in the near-infrared (“NIR”) field hasproved particularly effective in enabling the characterization of atarget petroleum product and its components in accordance with thisinvention.

According to one embodiment of this invention, in Step 3 a computerprogram is used which makes it possible to calculate the ranges ofrespective proportions of said components necessary for reconstituting aspectral datum (spectral datum and/or spectrum and/or spectra database)of the mixture belonging to the spectral range of Step 1 from thespectral datum (data) (spectral datum and/or spectrum and/or spectradatabase) of Step 2. Those skilled in the art have numerous computerprograms which make it possible to carry out these calculations. By wayof a purely illustrative and non-limiting example, we will citehereinafter the computer programs based on mathematical calculationsusing the Simplexe algorithm (any other algorithm for solving linearoptimization problems may also be used) and/or computer programs basedon mathematical calculations using the Nelder-Mead method (any otheralgorithm for solving non-linear optimization problems may also beused).

The characterization of a product according to this invention mayconsist in a determination and/or prediction of any chemical, physicalor physico-chemical characteristic of said product and its componentsand/or the identification of a type and/or family of components.

Applicant's Patent No. EP0742900 is the reference of the field oftopological spectral analysis. It describes a method for determining orpredicting a Px value, of a property of an X material or of a propertyof a product resulting from a process derived from said material or fromthe yield of said process, which method comprises measuring the D_(i)xabsorption of said material at more than one wavelength in the region of600 to 2600 nm, comparing the signals indicative of these absorptions ortheir mathematical functions with the signals indicating the Dimabsorptions at the same wavelengths or their mathematical functions fora certain number of S standards in a database for which said P propertyor yield is known, and selecting from the database at least one andpreferably at least 2 Sm standards having the Pm property, said Smstandard having the smallest average values of the absolute values ofthe difference at each wavelength i comprised between the signal for thematerial and the signal for the Sm standard in order to obtain the Pxvalue and averaging said Pm properties or yields, when more than one Smstandard is chosen.

Topological spectral analysis has many advantages over conventionalregressive mathematical methods. The numerical methods described formodeling of the physico-chemical properties of substances based onspectral analysis are correlative in nature and involve the regressionrelationships between the property(ies) studied. Among thesemultivariate analyzes are multilinear regression (MLR), main componentregression (PLR), canonical regression, and partial least squaresregression (PLS regression). In all cases, a relationship is soughtbetween the property and the spectrum which may be linear but which isusually quadratic or in an upper algebraic form with regressioncoefficients applied to each absorption. However, the establishment ofany regression requires a gradual calibration, since the approach isempirical and not supported by a theory.

These techniques have disadvantages, the main one being the need toestablish a strong correlation between spectrum and property and theirdifficulty in dealing with the positive or negative synergy between thecomponents contributing to this property. For example, to determine thechemical composition, for example by LINA (linear, isoparaffinic,naphthenic, aromatic) in a hydrocarbon load fed into a catalyticreformer, the use of the PLS technique based on NIR spectra wasdescribed. The model works well on the calibration set but the responseof the models when adding pure hydrocarbons, e.g., cyclohexane, is notsatisfactory since the model predicts variations in the isoparaffin andreverse naphthene content of those found experimentally. In addition,there are other practical difficulties, mainly due to the teed toidentify samples from families with the same type of relationshipbetween the spectra and the properties to be modeled. Thus, the modelmay be limited, especially with a nonlinear relationship betweenspectrum and property. Especially when there are limitations in the dataavailable, the accuracy of the model is reduced. The stability of themodel is also a problem, as well as the necessity to carry out laboriousrevisions when adding new standards to obtain the new model, especiallywhen adjusting to a new load to supply a process, thus the control of 6properties on 4 products coining out of a distillation unit requires 24models, each of which must be modified for each change in the feedstocknot included in the calibration. Another major disadvantage encounteredby these techniques arises when a point to be analyzed lies outside thepreviously established model; it is necessary to generate a new databaseand a new model per property, which makes this type of technique notonly not very reactive but also requires too many hours of work.

It should be noted that the topological spectral analysis as such hasnot actually evolved since the Applicant's patent number EP0742900.However, this invention also provides numerous improvements to saidtopological spectral analysis method. The characteristics of thistopological spectral analysis method, as well as its improvements andadvantages, will be described in detail in the description whichfollows, as well as in the examples, figures and Claims. Other purposesand advantages of this invention will appear in the description, givenhereafter in reference to the embodiments which are given asnon-restrictive indicative examples.

Understanding of this description will be facilitated by reference tothe attached FIGS. 1 to 10 and wherein:

FIG. 1 shows the NIR spectrum of a standard,

FIG. 2 shows an example of spectral database A.

FIG. 3 shows an example of spectral database B (polluting wavelengthshighlighted).

FIG. 4 shows an example of an improved spectral database A′ (spectraldatabase A in which the spectral data corresponding to the pollutingwavelengths have been removed),

FIG. 5 shows an example of an enlarged spectral database E (spectraldatabase A or A′ in which intergerms have been added),

FIG. 6 shows an example of an enlarged spectral database EE (spectraldatabase A and/or E in which extragerms have been added).

FIG. 7 shows an example of an enlarged spectral database EEI (spectraldatabase E and/or EE in which extragerms' have been added),

FIGS. 8 and 9 respectively show a graph and a table representing thediscriminating aggregates, and

FIG. 10 represents a spectral database of the type of that of FIG. 5 inwhich the measured characterizations of the standards and calculationsof the intergerms have been added.

In particular, all chemometric approaches for spectral analyses from thePrior Art require the establishment of a spectral database formed from avery large initial number of samples and/or standards. Although thePrior Art cites the establishment of a spectral database based on atleast 60 or at least 100 samples and/or standards, all the examplesdescribe bases consisting of a significantly higher number of samples.This number is even greater in the chemometric approaches usingregressive mathematical methods whose databases are made up of hundredsor even thousands of samples and/or standards. This invention, in oneparticular embodiment, makes it possible to overcome this previousrequirement, which opens up a considerable number of new applications asdemonstrated hereinafter.

Thus, in one particular embodiment, in a first step, the methodaccording to this invention consists of the preparation of a database ofspectra and/or spectral data of the target products and of theircomponents, preferably a spectral and/or a broad spectrum database E fora limited number of available standard materials (and thereforerepresenting the target products and/or their components).

This invention therefore applies more particularly to Near InfraredSpectroscopy (NIR). Indeed, NIR spectroscopy has many advantages overother analytical methods, for example in refineries, petrochemical orchemical sites as well as in all fields where the characterization ofchemicals, for example hydrocarbons, in particular, fuels, and can covera large number of repetitive applications with accuracy, speed andin-line. Moreover, the NIR region between 800 and 2500 nm contains allthe molecular information in the form of combinations and harmonics ofpolyatomic vibrations.

In a first step, a selected type of spectral analysis is carried out oneach of the standards (representative of the target product and/or itscomponents) and we proceed to populate the spectra and/or spectraldatabase A by recording the spectra (for example in digitized form),preferably NIR spectra, at several wavelengths (or wave numbers), forexample for a limited number of available standard materials.

An example of the constitution and representation of this mutualspectral database is described by means of FIGS. 1 and 2.

FIG. 1 shows the NIR spectrum of a standard upon which the absorbancemeasured as a function of the wave number may be visualized withspectral magnitude. Similar spectra are thus established identically foreach standard. In the present example of representation, nine standardshave been analyzed. From these spectra, a table (the spectral databaseA) is established, an exemplary representation of which is given in FIG.2 for a limited number of wave numbers.

Thus, in the table of FIG. 2 (which corresponds to a truncated view—twoparts of the table have been shown at different selected wavelengths),in the left column the references can be seen that make it possible toidentify the nine standards and in the first line the value of the wavenumbers or the ranges of wave numbers; the contents of the tabletherefore indicate the values of the spectral magnitude (in this case,the absorbances) which correspond to the “standard reference/wavenumber” pair. These spectral quantities may be any type of signalscharacterizing the spectra, for example the absorbances, transmittances,reflectances, etc.; the optical absorbances or densities being the mostcommonly used signals. By way of example, we may also mention as signalsthe derivatives of the absorbances or any other measurement resultingfrom another type of mathematical processing of said absorbances.

The limited number of available standards is usually dictated by thecustomer and/or end user who wish to use reliable reactive controlmethods while limiting the need for a large quantity of standards andthe need for an analysis by conventional methods.

A characteristic of the method according to this invention is that itthus makes it possible to overcome the need dictated by the Prior Art tolave a very large number of standards. For example, this invention makesit possible to characterize a sample product from less than 100available standards, or even less than 60 or even less than 50. Veryprobative results have even been obtained by means of this inventionfrom less than 40 available standards, even less than 30 or even lessthan 20. A minimum of 10 available standards is however preferred evenif this invention has already been successfully used with a minimum of 5available standards.

For this invention, the description thereof and the Claims hereinafter,it will be obvious to those skilled in the art that the spectra may beperformed as a function of the wavelengths (and/or ranges ofwavelengths) and/or as a function of the wave numbers (and/or ranges ofwave numbers), because the wave number is represented by the inverse ofthe wavelength.

For this invention, the description thereof and the Claims hereinafter,the standards will be equally well qualified as “germs” [“G”], the twoterms being interchangeable.

A second optional and preferred step according to this invention is thenthe elimination of “polluting” wavelengths and/or ranges of wavelengthsfrom the spectral database A. This step consists of

-   -   1. repeating at least twice, preferably at least three times,        more preferably at least five times the same spectral analysis        as that performed in the first step, and this should be        performed on at least one of the available standards, preferably        on at least two or even on all of said standards;    -   2. to construct a spectral database B from the measurements made        in point 1 above;    -   3. calculating for each selected standard from point 1 above and        for each wavelength and/or range of wavelengths (from the        spectral database A) the standard deviations (σ) from the        measurements recorded in the database B;    -   4. identifying the wavelengths and/or range of wavelengths in        database B for which the standard deviation is greater than a        predetermined value;    -   5. removing the measurements corresponding to the wavelengths        identified in point 4 above from spectral database A.

Thus, according to one preferred embodiment of this invention, the useof the second step above makes it possible to obtain an improvedspectral database A′; an example of an improved spectral database A′ isshown in FIG. 4.

An example of a representation of spectral database B is shown in FIG. 3as a table.

It can be seen that the same spectral analysis was repeated ten (10)times on the same sample and that the values for the correspondingspectral magnitudes were recorded in the table. The last three rows ofthe table correspond respectively and successively to

-   -   the mean value of the spectral magnitude VGSmean (“VGSm”) which        corresponds to the sum of the values of the spectral magnitudes        divided by the number (“n”) of analyzes performed        (VGSm=[ΣVGS]/n), with n=10 in this representation;    -   the standard deviation (“σ”) which corresponds to the difference        between VGSmax and VGSmin in each column of the table;    -   the ratio (σ/(VGSm/100)) whose value (in percentage) is        calculated by dividing the standard deviation by the value of        the mean spectral quantity, the result being multiplied by a        hundred.

Thus, the last line of the table makes it possible to identify thewavelengths and/or ranges of wavelengths in database B for which theratio (σ/(VGSm/100)) is greater than a predetermined value. According toone embodiment of this invention, within Table B one is able to identifythe columns (wavelengths and/or ranges of wavelengths) for which thevalue of the ratios (σ/VGSm/100)) is greater than 2% (preferably greaterthan 1.5% or even 1%); then, said columns are deleted from database A,namely the values of the spectral magnitude corresponding to the“polluting” wavelengths. The corresponding columns (i.e., those whosewavelength and/or range of wavelengths are identical) will then beeliminated from spectral database A. It should be noted that in theabove examples, Tables A and B are representations which do not have anyactual relationships between them: It should also be noted that Tables Aand B have been truncated to give a visual representation; in reality,said tables comprise a multitude of columns representing the wavelengthsand/or ranges of wavelengths extracted from the corresponding spectrumas detailed further in the description.

An example of the representation of the improved spectral database A′ isthus illustrated in FIG. 4.

An essential characteristic of the method according to this invention isthat establishing the improved spectral database A′ at this stage doesnot need to reference and/or make the least correlation with thechemical and/or physico-chemical properties of the standards. Indeed,this second stage is totally independent.

A preferred third consecutive step of the method according to thisinvention is the actual enlargement of spectral database A (or theimproved spectral database A′). This step consists in generatingsynthetic standards (also called “intergerms” [“IG”]) based on theavailable standards and their spectral magnitude values. For example, togenerate these IGs, combinations of several available standards from thefirst step above can be performed and this will populate the spectraldatabase A (or the improved spectral database A′) by means of saidcombinations. These combinations may be performed randomly or in adirected manner as described further in the text. Said combinations mayconsist of any kind of mathematical process applied to the spectralmagnitude values of the G standards. According to one preferredembodiment of this invention, said combination consists of a barycenterof the spectral magnitude values (“VGS”) of at least two standards. Itis possible, for example, to carry out these combinations between two,three or a number higher than that of the starting standards available,preferably between all available starting standards.

An example of a corresponding formula for generating a syntheticstandard (IG) from the G standards (to which the VGSs correspond) is

[ΣRi×VGSi]/[ΣRi]

in which i is an integer from one to the number of G standards selectedfor this combination and R being a real number such that

[ΣRi]>0, and

|[ΣR*i]|/[ΣRi]<0.3, preferably <0.15,

And with R* representing only real negative numbers.

The latter formula can also be expressed as the absolute value of thesum of the real negative numbers divided by the sum of all the realnumbers.

According to one preferred embodiment of this invention, at least one ofthe Ri is a real negative number (R*).

By doing so, spectral database A (or the improved spectral database A′)can be enlarged by means of synthetic standards (also called“intergerms” or “IGs”), thus obtaining an enlarged spectral database E.

According to one preferred embodiment of this invention, when the numberof standards of spectral database A (or A′) is equal to “N”, the numberof intergerms IG is at least greater than 1.5 N. Preferably greater than2 N, more preferably greater than 5 N, or even greater than 10 N.

An example of a representation of the enlarged spectral database E isshown in FIG. 5 as a table. It can be seen that synthetic standards (orintergerms “IG”) have been generated by mathematical combinations andthat the values corresponding to spectral magnitude have been recordedin Table E. For example, we may see in Table E (FIG. 5):

-   -   six intergerms “IG” (I2G022, I2G011, I2G036, I3G038, I3G025 and        I3G019;    -   in columns 3 to 5, the germs used to generate each of said        intergerms;    -   in column 2, the weighting applied to the germs selected for the        calculation of the VGS of the intergerms (for example, for the        calculation of intergerm 12G036), a weighting of (0.44 times the        germ A0000008+0.56 times the germ A0000004)) is applied.

An essential characteristic of the method according to this invention isthat establishing the enlarged spectral database E at this stage doesnot need to reference and/or make the least correlation with thechemical and/or physico-chemical properties of the standards. In fact,this expansion step is totally independent.

A fourth additional, optional, and preferred step according to thisinvention then consists in further expansion of the spectral database Aor the enlarged spectral database E by means of another type ofsynthetic standards which we will call “extragerms” (“EG”). This step isparticularly pertinent when the target product to be analyzed contains aplurality of chemical compounds.

It consists, in a first sequence, of recording the spectral data of atleast one spectrum corresponding to one (or more) of the chemicalcompounds of the target product (also called “Pole(s)”). Then, in asecond sequence, the spectral database is further enlarged by using saidpole(s) and by combining them with the germs “G” (a combination isperformed of values of their spectral magnitude VGS).

This second sequence consists of generating synthetic standards (alsocalled “extragerms” [EGs]) from the available Pole(s) and standards andthe values of their spectral magnitude. For example, in order togenerate these EGs, it is possible to combine the Pole(s) and severalavailable standards from the first step above and populate the spectraldatabase A and/or E by means of said combinations. These combinationsmay be performed randomly or in a directed manner as described furtherin the text. Said combinations may consist of any kind of mathematicalprocess applied to the values of the spectral magnitudes of the Gstandards and of the Pole(s). According to one preferred embodiment ofthis invention, said combination consists of a barycenter of thespectral magnitude values (“VGS”) of the selected G standards and of thePole(s). It is possible, for example, to carry out these combinationsbetween at least one Pole and one, two, three or a higher number ofstarting standards, preferably with all the available startingstandards. These combinations will preferably be carried out with allthe available Poles, preferably with all the Poles corresponding to allthe chemical compounds constituting the target product.

An example of a formula corresponding to the generation of an EG-typesynthetic standard from Pole(s) and G standards (to which the VGScorresponds) is

[ΣRi×VGSi+ΣRj×VGSj]/[ΣRi+ΣRj]

in which i is an integer from one to the number of G standards selectedfor this combination, j being an integer from one to the number of pole(s) chosen for this combination and R is an integer such that

[ΣRi+ΣRj]>0, and

|[ΣR*i]|/[ΣRi+ΣRj]<0.3, preferably <0.15,  (I)

with R* representing only real negative numbers.and preferably, each Rj must be such that the ratioRj/[ΣRi+ΣRj] always falls between the opposite of the minimum contentand the maximum content percentage by weight of the Poles j in thetarget product.

The formula (I) above can also be expressed as the absolute value of thesum of the real negative numbers “i” divided by the sum of all theintegers. According to one preferred embodiment of this invention, atleast one of the Ri is a real negative number (R*).

By doing so, this makes it possible to enlarge the spectral database Aand/or E by means of synthetic standards “EG” (“Extragerms”) and in thisway, obtain an enlarged spectral database EE. Optionally, said Poles andtheir VGS may also be entered into the spectral database EE but thisdoes not constitute a preferred embodiment according to this invention.

According to one preferred embodiment of this invention, when the numberof standards of the spectral database A (or A′) is equal to “N” and thenumber of “Poles” is equal to “M”, the number of Extragerms “EG” is atleast greater than N×M, preferably greater than 1.5 N×M, preferablygreater than 2 N×M.

According to one preferred embodiment of this invention, the number ofPoles is lower than 15, for example lower than 10.

According to one preferred embodiment of this invention, the number ofPoles is lower than 0.2 times the number of standards, for example lowerthan 0.1 times the number of standards.

An example of a representation of the expanded spectral database EE isshown in FIG. 6 as the Table EE. It can be seen that the “Poles” as wellas the generation of synthetic standards “EG” (extragerms) bymathematical combinations and the corresponding spectral magnitudes havebeen recorded in the table. For example, we may see in Table EE (FIG.6):

-   -   six extragerms “EG” (MEG001 to MEG006);    -   in column 2 (“Pole”), the reference of the Poles used (for        example, PAL054 is a particular type of alkylate used in the        composition of gasolines constituting the standards of the        database);    -   in column 3, the reference of the germ used to generate each of        said extragerms;    -   in column 4, the weighting applied to the Poles (X)−the        weighting applied to germs is therefore (1−X). For example, for        calculation of the extragerm MEG001, a weighting of (0.15 times        the Pole PAL054+0.85 times the germ A0000009) was applied.

An essential characteristic of the method according to this invention isthat establishing the expanded spectral database EE at this stage doesnot need to reference and/or make the least correlation with thechemical and/or physico-chemical properties of the standards. In factthis expansion step is totally independent.

A fifth additional, optional, and preferred step according to thisinvention also consists of a further enlargement of the expandedspectral database E and/or EE by means of another type of syntheticstandards which we shall call “extragerms” (“EG′”) Again, this step isparticularly pertinent when the target product to be analyzed contains aplurality of chemical compounds.

It consists, in a first sequence, of recording the spectral data of atleast one spectrum corresponding to one (or more) of the chemicalcompounds of the target product (also called “Pole(s)”).

Then, in a second sequence, an additional enlargement of the spectraldatabase E or EE is carried out using said Pole(s) and by combining themwith the intergerms “IG” (combination of their VGS).

This second sequence consists in generating synthetic standards (alsocalled “extragerms” [“EG”]) from the Pole(s) and the “intergerm”standards “IG” (and optionally from the germs “G”) and their spectralmagnitude values. For example, in order to generate these EG′,combinations of the Pole(s) and several intergerms “IG” of the thirdstep above (and optionally of “G” germs from the first step) may beperformed and the spectral database E and/or EE may be populated bymeans of said combinations.

These combinations may be performed randomly or in a directed manner asdescribed further in the text. Said combinations may consist in any kindof mathematical treatment applied to the values of spectral quantitiesof the synthetic (intergerms) “IG” standards and of the Pole(s) (andoptionally of the “G” germs).

According to one preferred embodiment of this invention said combinationconsists of a barycenter of spectral magnitude values (“VGS”) of theintergerms IG and of the Pole(s) (and optionally of the germs “G”). Itis possible, for example, to carry out these combinations between atleast one Pole and one, two, three or a greater number of the “IGs” ofthe third step, preferably with all the “IGs”: and optionally with atleast one of the germs “G”, preferably with all the germs “G”. Thesecombinations will preferably be carried out with all the availablePoles, preferably with all the Poles corresponding to all the chemicalcompounds constituting the target product.

An example of a formula corresponding to the generation of an EG′-typesynthetic standard from Pole(s) and IG synthetic standards (to which theVGS corresponds) is

[ΣRi×VGSi+ΣRj×VGSj+ΣRk×VGSk]/[ΣRi+ΣRj+ΣRk]

k being an integer from one to the number of synthetic standards IGchosen for this combination, i being an integer ranging from 0(preferably one) to the number of G standards selected for thiscombination, where j is an integer ranging from one to the number ofPole(s) chosen for this combination and R being a real number such that

[ΣRi+ΣRj+ΣRk]>0, and

|[ΣR*i]+[ΣR*k]|/[ΣRi+ΣRj+ΣRk]<0.3, preferably <0.15,  (II)

preferably with Rk being always positive.with R* representing only real negative numbers,AND preferably, each Rj must be such that the ratio

Rj/[ΣRi+ΣRj+ΣRk]

always falls between the opposite of the minimum content and the maximumcontent percentage by weight of the Poles j in the target product.

The formula (II) above can also be expressed as the absolute value ofthe sum of the real negative numbers “i” divided by the sum of all theintegers. According to one preferred embodiment of this invention, atleast one of the Ri is a real negative number (R*).

By doing so, this makes it possible to enlarge the spectral database Eand/or EE by means of synthetic standards “EG′” (“Extragerms”) and inthis way, obtain an enlarged spectral database EEI. Optionally, saidPoles and their VGS may also be entered into the spectral database E butthis does not constitute a preferred embodiment according to thisinvention.

According to one preferred embodiment of this invention, when the numberof synthetic standards IG of the spectral database E is equal to “Z” andthe number of “Poles” is equal to “M”, the number of Extragerms “EG” isat least greater than Z×M, preferably greater than 1.5 Z×M, preferablygreater than 2 Z×M.

According to one preferred embodiment of this invention, when the numberof synthetic standards of the IG of spectral database E is equal to “Z”,the number of germs G is equal to N and the number of “Poles” is equalto “M”, the number of Extragerms' “E.G.,′” is at least greater thanZ×M×N, preferably greater than 1.5 Z×M×N, preferably greater than 2Z×M×N.

According to one preferred embodiment of this invention, the number ofPoles is lower than 15, for example lower than 10.

According to one preferred embodiment of this invention, the number ofPoles is lower than 0.2 times the number of standards, for example lowerthan 0.1 times the number of standards.

An example of a representation of the expanded spectral database EEI isshown in FIG. 7 as a table. It can be seen that the “Poles” as well asthe generation of synthetic standards “EG′” (extragerms′) bymathematical combinations and the corresponding spectral magnitudes havebeen recorded in the table.

For example, we may see in Table EEI (FIG. 7):

-   -   six extragerms' “EG′” (MEP001 to MEP006);    -   in column 5 (“Pole”), the reference of the Poles used (for        example, PAL037 is a particular type of alkylate used in the        composition of gasolines constituting the standards of the        database).    -   in columns 2 to 4, the reference of the intergerms (combinations        of germs) used to generate each of said extragerms;    -   in column 6, weighting applied. For example, for the calculation        of the extragerm MEP004, a weighting of [0.9 times one intergerm        (corresponding to 0.306 times the germ A00000061−0.0530 times        the germ A0000009+0.647 times the germ A0000002)+0.1 times the        Pole PAL037] is applied.

An essential characteristic of the method according to this invention isthat establishing the expanded spectral database EEI at this stage doesnot need to reference and/or make the least correlation with thechemical and/or physico-chemical properties of the standards. In factthis expansion step is totally independent.

Therefore, this invention also relates to a method to generate andimprove a spectral database (preferably used in steps one and 2 of theM2 Process for preparation of the aforementioned petroleum targetproduct) which can be used in a method to characterize a target productand/or its components by topological spectral analysis from a limitednumber of available standards,

in a first step the method consists of

-   -   performing the same spectral analysis on said standards, and    -   constituting from the spectra obtained a spectral database A        with several wavelengths and/or ranges of wavelengths,    -   characterized in a second optional step, in which the        wavelengths and/or ranges of wavelengths of “polluting”        wavelengths in spectral database A are deleted from said        spectral database A, the second step consisting of        -   1. repeating at least twice, preferably at least three            times, more preferably at least five times the same spectral            analysis as that performed in the first step, and this            should be performed on at least one of the available            standards, preferably on at least two or even on all of said            available standards;        -   2. to construct a spectral database B from the measurements            made in point 1 above;        -   3. calculating for each selected standard from point 1 above            and for each wavelength and/or range of wavelengths (from            the spectral database A) the standard deviations (a) from            the measurements recorded in the database B;        -   4. identifying the wavelengths and/or range of wavelengths            in database B for which the standard deviation is greater            than a predetermined value; and        -   5. removing the measurements corresponding to the wavelength            identified in point 4 above from the spectral database A and            thus obtain an improved spectral database A′,            -   and also characterized by a third consecutive and                preferred step consisting in the enlargement of spectral                database A (or from the improved spectral database A′).                This step consists of performing combinations of several                standards from the first step and populating spectral                database A (or the improved spectral database A′) by                means of said combinations (called synthetic standards                or intergerms “IG”) and obtaining thusly an enlarged                spectral database E,

and also characterized by a fourth consecutive and optional stepconsisting in enlarging spectral database E. This step consists of afirst sequence to add to the enlarged spectral database E at least onespectrum corresponding to at least one (or more) of the chemicalcompounds of the target product (also called “Pole(s)”) and a secondsequence to perform mathematical combinations of the Pole(s) with atleast one G standard from the first step and/or at least one IG standardfrom the third step and to populate spectral database E with saidcombinations (respectively called either synthetic standards extragerms“EG”, or synthetic standards extragerms “EG′”) and to obtain thusly anenlarged spectral database EE (or EEI).

After having built up the enlarged spectral database in accordance withthe methodology developed above, it is possible to use any kind ofconventional mathematical analysis to characterize a sample from theexpanded spectral database.

According to one preferred embodiment of this invention, before thischaracterization, an additional intermediate step then consists ofdefining an effective discriminant method making it possible todemonstrate homogeneous subgroups of products that preferably obey thesame types of properties-spectra bonds due to a strong analogy ofmolecular structure.

Discriminant methods can only be based on techniques for mathematicalanalysis (for example, factor analysis and/or principal componentanalysis). Although some of these mathematical methods may be useful,this invention preferably uses at least one empirical step to performthis type of discrimination. This empirical step should be based onvisual analysis of the spectra of standards and/or the aforementionedpoles; although this is not a preferred embodiment of this invention,this visual analysis could also be done on reconstituted spectra (fromtheir calculated VGS) of intergerms and/or extragerms. This empiricalstep makes it possible to highlight the small differences between thespectra in question, after verification that differences may besynonymous with the existence of homogeneous subgroups of products evenif one might have originally thought that the entire population ofproducts was homogeneous. Therefore, this technique allowsdiscrimination to highlight the differences between the products ofwhich the final user has no knowledge.

To recap, a key feature of the process to establish the extendedspectral database according to a preferred embodiment of the inventionabove is that it was not necessary to reference and/or make anycorrelation with chemical and/or physico-chemical standards. Accordingto one preferred embodiment of this invention, it is exactly the samefor the discriminant step described herein.

Aggregates

Thus, according to one embodiment of this invention, the discriminationstep consists of defining, from the spectral database (preferably, theenlarged version), the aggregates (preferably at least two aggregates),the n-dimensional spaces representing the combinations of the saidaggregates (preferably planes—or two-dimensional spaces—representingpairs of aggregates), and the corresponding spectral boxes. According toone preferred embodiment of this invention these aggregates and/or then-dimensional spaces represent combinations of said aggregates and/orthese spectral boxes define the spectral range of the target product andtherefore the fact that the final mixture is in accordance with asignificant set of specifications of the target product.

According to one embodiment of this invention, the discriminant methodalso includes at least two specific preferred characteristics:

-   -   1. in that said method involves an iteration phase during which        the effectiveness of the spectral box is verified and therefore        the pertinence of the selected aggregates; and    -   2. the fact that the aggregates are constructed from at least a        visual analysis of the shape of the spectra which is then used        to build equations from the aggregates based on the values of        the spectral magnitude of the VGS.

Aggregates are defined as mathematical functions of the values of thespectral magnitude from the enlarged spectral database for groupingand/or discriminating and/or separating product families within theextended spectral database.

These aggregates can be represented generically by the function

Agg=f(VGSi).

According to one preferred embodiment of this invention, said functionsatisfies the equations of this type

$\frac{\Sigma_{k = 1}^{n}\Sigma_{i = 1}^{p}{aiWi}^{\propto}{Wk}^{\beta}}{\Sigma_{i = 1}^{q}{aiWi}^{\propto}}$

or preferably of the type in which

$\frac{\Sigma_{i = 1}^{p}{aiWi}^{\propto}}{\Sigma_{i = 1}^{q}{aiWi}^{\propto}}$

-   -   W represents the discriminant values of the spectral magnitude        VGS,    -   a are positive real numbers,    -   p and q represent the selection of the VGS to the wavelengths        and/or relevant wavelength ranges for the discrimination step,        and    -   ∝ and β are exponents between ⅓ and 3.

As regards the iteration phase during which the effectiveness of thespectral box is verified and therefore the relevance of the selectedaggregates, it suffices to add to the predetermined spectral databasecolumns representing the equations of discriminating aggregates,calculating the value of said aggregates for each of the standardsand/or intergerms and/or extragerms and or poles from the spectraldatabase, to make the graphs (preferably in two-dimensional spaces foreach pair of aggregates), and to thus see whether discrimination has ledto the identification of homogeneous product subgroups. Thisdiscrimination step makes it possible to divide the spectral databaseinto several (at least two) different families (homogeneous productsubgroups), preferably with at least three different families.

By way of example, FIGS. 8 and 9 respectively show

-   -   a graph whose abscissa/ordinate axes correspond to two        discriminating aggregates, and    -   a table of corresponding values whose columns represent several        discriminating aggregates, the first two of which were used in        the creation of the graph (FIG. 8).

These Figures clearly show how we manage to highlight severalhomogeneous product subgroups: which makes it possible to select thespectral range of the target product.

Therefore, this invention also relates to a method for characterizing aproduct by topological spectral analysis.

The characterization of a product according to this invention mayconsist of determining and/or predicting any chemical, physical orphysico-chemical characteristic of said product.

According to one preferred embodiment of this invention, the first stepwas therefore characterized by establishing a spectral database,preferably an enlarged spectral database as described in thisdescription.

As already indicated above, the graphic representations of the databases(tables) in the accompanying Figures are truncated views because inreality said databases include a plurality of columns representing thewavelengths and/or ranges of wavelengths (or as an equivalent, the wavenumbers or range of wave numbers) extracted from the correspondingspectra.

According to one preferred embodiment of this invention, the number ofwavelengths selected may be from two to 1000, for example from five to200 or from 40 to 80.

The wavelengths chosen may be at regular intervals such as one to 50 nmor every 10 to 50 nm or every 15 to 35 nm or every one to 5 nm or everynanometer, or they may be at irregular intervals, for example, atintervals of one to 200 nm, for example from one to 100 or from one to50, in particular, from two to 50 or from four to 50 or from 10 to 60nm, which may be selected or random due to a variation in the shape ofthe spectral curve at that wavelength e.g., a peak, a valley or shoulderor chosen with chemical or statistical criteria, such as factoranalysis. The wavelengths may be in the region 600 to 20000 nm, forexample from 625 to 2600 nm, for example from 800 to 2600 nm, inparticular from 1500 to 2600 or from 2000 to 2550 nm. The wave numberscan be in the region of 16600 to 500, for example from 16000 to 3840cm-1, for example from 12500 to 3840 cm-1, in particular from 6660 to3840 or from 5000 to 3900 cm-1; the corresponding frequencies in Hertzcan be obtained by multiplying these wavelengths by 3×10 (exp) 10 cm/s.

Before you can identify and/or predict the property of a sample, it isobviously necessary to measure the values of the said property to thestandards and, optionally, to the poles. Thus, in one embodiment of thisinvention, the chemical, physical and/or physico-chemical standards (andoptionally the poles) are determined using conventional analyticaltechniques. By way of a non-limiting example of the conventionalanalytical techniques, we may mention gas chromatography for chemicalcompositions. Although it goes without saying that the standards areselected to cover the range in which the method is to be used, in apreferred embodiment, this invention provides for working with a limitednumber of standards through the methodology of enlarging theaforementioned spectral database.

Thus, in one preferred embodiment of this invention, the values of thedesired properties measured for said standards (and optionally for thepoles) are added to the spectral database: when the spectral database isenlarged, the values of said properties for synthetic standardsintergerms (and optionally for the extragerms) are then calculated fromthe formulas used to generate the synthetic standards; this calculationis done simply by replacing the spectral magnitude values VGS with themeasured values of said properties of the standards (and optionally thepoles) used in the formulas (and optionally, for the extragerms, by thevalues already calculated for the intergerms). This leads to a spectraldatabase consisting of a number of points (standards and optionally theintergerms, the poles and the extragerms) which are associated with thedesired properties (measured and calculated). An example of anembodiment (truncated view) is given in FIG. 10.

This is illustrative of an enlarged spectral database E consisting ofstandards (A) and intergerms (IG). The table has been supplemented bythe characteristics of the desired target products, namely RON and MONvalues (Research Octane Number (RON) and Motor Octane Number (MON)).These characteristics were therefore measured for the standards andcalculated for the intergerms.

In the description of EP0742900, the signals are compared, e.g., theabsorptions (or their derivatives) for the unknown sample, with thesignals, e.g., absorptions (or their derivatives) at the same wavelengthof the standards, and the standards having the smallest differences arechosen. Then the properties of these standards chosen are averaged todetermine the property of the unknown sample. Therefore, a calculatedspectrum is reconstituted from the target product to which thecharacteristic (property) is thus calculated.

According to a preferred embodiment of this invention, this comparisonof signals is therefore not performed on the entire spectral database,but only the portion of the spectral database representative of thehomogeneous subgroup to which the sample belongs. This is donepreferably by using the above-mentioned discriminant method(discriminant aggregates) that is defined in this part of the spectraldatabase.

Then, the signals are compared, e.g., the absorptions (or theirderivatives or any other value of spectral magnitude) for the unknownsample (target product), with the same signals and at the samewavelength of the standards and/or intergerms and/or extragerms and/orpoles belonging to the same homogeneous subgroup, and the standardsand/or intergerms and/or extragerms and/or the poles having the smallestdifferences is chosen in the spectral database.

Whatever the method is used, the points nearest to the target productwill later be called “close neighbor”. Then, for example, averaging suchproperties can make these standards and/or intergerms and/or extragermsand/or poles selected may be used to determine the desiredcharacteristic (property) of the unknown sample.

In accordance with one particular embodiment of this invention, theclose neighbor chosen are those with the smallest average values of theabsolute difference at each wavelength i between the value of spectralmagnitude (represented for example by the absorbance or a derivativethereof) Wix for the target product (sample/unknown product) and thesignal corresponding to Wim for the close neighbor. The averages mayrelate for example to the average value of Wix−Wim (regardless of itssign, i.e., an absolute difference), or of (Wix−Wim) exp2. Each closeneighbor in the spectral database for the type of product in question,we find the average difference as described and we choose the closestneighbor having the smallest average differences, namely at least onebut preferably two, up to 1000 of the smallest, for example two to 100or two to 20, but especially from two to 10 and especially two to 6 ofthe smallest. This selection of closest neighbors can be performed byany known method, for example, the methods described in the descriptionof patent No. EP0742900 can be used advantageously (for example todetermine the proximity index).

According to one particular embodiment of this invention, the number ofclose neighbor may be equal to one, preferably greater than or equal totwo, even preferably greater than or equal to three.

According to one embodiment of this invention, the number of closeneighbor is less than or equal to 50, for example less than or equal to20, or even 10.

As stated previously, from the time the “close neighbor” points wereselected, one can easily average the selected properties of these closeneighbor (standard and/or intergerms and/or extragerms and/or poles) todetermine the property of the unknown sample (the target product).Therefore, a calculated spectrum is reconstituted from the targetproduct to which the characteristic (property) is thus calculated.

However, and this is a preferred embodiment of this invention, theapplicant has unexpectedly found a significant improvement in theaccuracy and robustness of its method for determining the desiredcharacteristic (e.g., a property) of a target product when performing aweighted average of the properties of these “close neighbor” points(which may be standards and/or intergerms and/or extragerms and/orpoles), said weighting being an inversely proportional linear ornon-linear function to the distance between the sample (“the targetproduct”) and the “close neighbor” points selected; this weighting mayfor example be represented by the formula

${POND} = \frac{\frac{1}{{di}^{\alpha}}}{\Sigma_{1}^{n}\frac{1}{{di}^{\alpha}}}$

With α being a positive number, preferably between 0.5 and 1.5, di isthe distance between the target product and the close neighbor i, and nis the total number of close neighbor.

Therefore, a weighting of this kind in the measured properties (andoptionally calculated) of “close neighbor” is applied to obtain theproperty of the target product.

Therefore, a calculated spectrum is reconstituted from the targetproduct to which the characteristic (property) is thus calculated.

In other words, the calculation of characteristic Z of the targetproduct is achieved through the corresponding characteristics Zi of theclose neighbor points, while allowing characteristics of said closeneighbor points a much greater weight in said calculation in that theyare closer to the target product.

Thus, this invention also provides a method of characterizing a targetproduct comprising the following steps:

-   -   1. Establishment of a spectral database comprising samples,        their spectra and their measured characteristics (“CAR”, for        example the property “P”),    -   2. Spectral analysis of the target product and comparison of the        obtained spectrum (Spectrum PC) with the spectral data from the        database,    -   3. Identification of the “close neighbor” points of the target        product, and    -   4. Calculate by topology the characteristic of the target        product (CARpc/top, for example the property Ppc/top) according        to the corresponding characteristic close neighbor points.        characterized in that the calculation of step 4 is based on a        weighting related to the inverse of the distance between the        target product and the close neighbor points.

One can use the method of the invention to determine more than oneproperty P at a time. For example at least two, in particular from oneto 30 for example two to 10 properties at a time. Of course we can usedifferent numbers of standards chosen for each property.

In another preferred embodiment of this invention, the Applicant hasdiscovered a particularly effective alternative method.

This method involves combining one of the topological characterizationmethods of the aforementioned target product with any mathematical modelthat differs from the topological methods (preferably a regressionmodel) and that makes it possible to characterize the target productfrom the spectral magnitude values VGS (for the same property).

This method thus requires prior establishment of a mathematical modelthat can calculate the properties of products based on spectralmagnitude values (VGS) from the database, preferably a regression model(product characterization from the pre-established spectral database);this spectral database can be either the aforementioned database A orpreferably database A′, E, EE or EEI, or a selection of said databases.Preferably, this database will be the same as that used for thetopological method.

This alternative method for characterizing a target product comprisesthe following steps:

-   -   1. Establishment of a spectral database comprising samples,        their spectra and their measured characteristics (“CAR”, for        example the property “P”),    -   2. Spectral analysis of the target product and comparison of the        obtained spectrum (Spectrum PC) with the spectral data from the        database,    -   3. Identification of the “close neighbor” points of the target        product,    -   4. Calculation by topology        -   4.1. of the characteristic of the target product (CARpc/top            top, e.g., property PPC/top) and        -   4.2. of its thusly calculated spectrum (spectrum PCcalc),    -   5. Establishment from the spectral database of a mathematical        model to calculate the characteristic of a product from the        spectral database (CAR/mod, for example property P/mod)    -   6. Calculation of the characterization of the target PC product        using the following        formula=CARpc=CARpc/top+[CARpc/mod−CARpccalc/mod]        -   with            -   CARpc being the calculated value of the characteristic                of the desired target product.            -   CARpc/top is the value calculated by topology (close                neighbor points) of the characteristic of the target                product            -   CARpc/mod being the value calculated by the mathematical                model of the characteristic of the target product and            -   CARpccalc/mod being the value calculated by the                mathematical model of the characteristic of the                calculated target product (using the spectral data                obtained in point 4.2).

The characterization of a product according to this invention mayconsist in a determination and/or prediction of any chemical, physicalor physico-chemical characteristic of said product and/or theidentification of a type and/or family of products.

For example, the presence of individual chemical compounds within onecompound may be determined as well as their concentrations; any type ofproperty can also be determined, some of which are exemplified below.

Thus the method can be used for the physico-chemical determination orprediction regarding at least one feedstock or a product used in anindustrial oil refining process and/or petrochemical operations orobtained in aid thereof. The process can be a hydrocarbon conversion orseparation process, preferably a process of reforming or catalyticcracking or hydro-processing, or distillation or blending. Inparticular, the following may be used to determine at least one propertyof a feedstock and/or to predict and/or determine at least one propertyand/or the yield of a product from a certain number of differentprocesses such as processes for separating petroleum products such asatmospheric distillation, vacuum distillation or distillativeseparation, under greater than atmospheric pressure, and thermal orcatalytic conversion, with or without partial or total hydrogenation ofa petroleum product, such as catalytic cracking, for example, fluidcatalytic cracking (FCC), hydrocracking, reforming, isomerizationselective hydrogenation, visbreaking or alkylation. In particular, thisinvention applies to the mixture of components of the target products(e.g., target petroleum products, for example, fuels) in any suitableplace, for example a refinery, an oil terminal and/or any device using atarget product made of a mixture of components prepared in batchesand/or preferably in-line.

The use of the method in mixing operations involving the productionand/or determination of at least one property of a liquid hydrocarbonmixture (optionally with other additives, such as alkyl ethers) is ofparticular value. This method may comprise or may not comprise thedetermination of each component of the mixture of a mixture index forthe property sought. In this method as applies to the mixture, one cansimply obtain the blend index by calculation and without having toprepare the physical mixtures of standards other than those contained inthe database. The mixing indices may be combined linearly (ornonlinearly) in the areas of stability to determine from the value ofthis combination, a value for at least one property of the resultingmixture. The mixture can be created by mixing at least two compoundschosen from butane, hydrogenated steam cracked gasoline, isomerate,reformate, methyl-ter-butyl-ether (MTBE) and/or tert-Amyl methyl ether(TAME) and/or ethyl-ter-butyl-ether (ETBE), derived by FCC gasoline,ethanol and/or bioesters. This process may be repeated by digitallyadding the other components separately to the liquid hydrocarbon base todetermine a series of mixing indices and then determining from theseindices the properties of the multi-component mixture.

Examples of properties that can be determined and/or predicted are thefollowing: for automotive fuels/gasolines, at least one of the ResearchOctane Number (RON), the Motor Octane Number (MON) and/or theirarithmetic mean, with or without additives and/or the content ofmethyl-t-butyl ether or methylisoamyl ether and/or benzene.

For automotive fuels/gasolines, at least one of the vapor pressures,density, volatility, distillation curve, such as the percentagedistilled at 70° C. and/or 100° C., the oxygen content or the content ofbenzene or sulfur, the chemical composition and/or for example, the gumcontent expressed in mg/100 ml (especially to determine these propertiesfor use in the mixing operations).

For diesel or gas oil fuels, at least one cetane number (e.g.,measurement at the motor), the calculated cetane index, cloud point, the“discharge point”, the filtering ability, the distillation curve, thedensity, e.g., 15° C., the flash point, e.g., the viscosity at 40° C.,the chemical composition, the sensitivity to additives and thepercentage of sulfur.

For the distillation of products produced from crude oil, e.g., atatmospheric pressure, at least one of densities, percentage of sulfur,viscosity at 100° C., the distillation curve, the paraffin content, theresidual carbon content or Conradson carbon content, the content ofnaphtha, the flash point of the oil, the cloud point for diesel fuel,e.g., light gas oil and/or the viscosity at 100° C. and/or the contentsulfur for the atmospheric residues and the yield for at least one ofthe cuts, gasoline (bp. 38-95° C.), benzene (bp. 95 at 149° C.), naphtha(bp. 149 to 175 C), kerosene (bp. 175 to 232° C.), light gas oil (bp.232 to 342° C.), heavy gas oil (bp. 342 to 369° C.) and that of theupper atmospheric residue to 369° C.

For at least one of a feedstocks or products of a catalytic crackingprocess e.g., an FCC process, at least one of densities, percentages ofsulfur, the aniline point, the diesel index, the fuel index, viscosityat 100° C., refractive index at 20° C. and/or at 60° C. the molecularweight, the distillation temperature e.g., the distillation temperatureat 50%, the percentage of aromatic carbon, the total nitrogen contentand the factors characterizing the crackability of the feedstock e.g.,Kuop, the crackability factor, the cokability factor and the yield e.g.,in gas, gasoline, gas oil or residue. Thus, it is possible to determinethe yields and/or properties of the various products obtained bydistillation of the cracked products such as RON and/or MON without ananti-knock additive for gasoline cutting and viscosity at 100° C. forthe distillation residue.

For at least one of the products or feedstock from a catalytic reformingprocess, at least one of the densities, the distillation temperaturesand/or chemical compositions (expressed in percentages) of linearsaturated hydrocarbons, isoparaffins, naphthenes, aromatics and olefins.

For at least one of a products or a feedstocks for a gasolinehydrogenation process, at least one of the densities, the distillationtemperature, RON and/or MON, the gasoline vapor pressure withoutanti-knock additives or lead, volatility, chemical composition(expressed as a percentage) in linear saturated hydrocarbons,isoparaffins, naphthenes, aromatic substances such as benzene andmono/di-substituted benzene, olefins such as cyclic and non-cyclicolefins, diolefins, and the index of maleic anhydride.

It must be obvious to the skilled person that this invention allowsembodiments in many other specific forms without departing from thescope of the invention as claimed. In this way, these embodiments mustbe considered to be for illustrative purposes being able to be modifiedwithin the domain defined by the scope of the attached Claims.

1. Method for preparation of a target product for batch and/or in-linemixing of its “n” components from different flows from said componentsat controlled concentrations and/or flow rates, with optionalincorporation of additives, said target product having to be marketedwith a set of ranges of physico-chemical characteristic values, methodin which a batch and/or continuous mixer is fed said components atcontrolled concentrations and/or flow rates, this process beingcharacterized in that:
 1. at least one spectral datum characterizing thetarget product is available and which defines its spectral range 2.spectral datum (data) is (are) available, each individuallycharacterizing at least two—preferably all the “n”—components of thetarget product,
 3. a computer program is used which makes it possible tocalculate the ranges of respective proportions of said componentsnecessary for reconstituting a spectral datum of the mixture belongingto the spectral range of Step 1 from the spectral datum (data) of Step2, and
 4. the ranges of respective proportions of the constituents ofstep 3 are used to control the concentrations and/or flow rates of thecomponents fed into the mixer so as to prepare the target product. 2.Method according to claim 1 characterized in that the spectral data aremeasured using the same type of spectral analysis, preferably using thesame type of spectrometer.
 3. Method according to claim 1 characterizedin that the spectral data are spectra and/or spectral databases. 4.Method according to claim 1 characterized in that the spectral data areobtained from spectral analysis chosen from NMR, Raman, infrared (IR),near-infrared (NIR) and/or UV/Visible, preferably near-infraredtopological spectral analysis (“NIR”).
 5. Method according to claim 1wherein the target product comprises a major component whoseconcentration by weight in the target product is the highest and atleast one minor component and characterized in that the spectral data ofthe minor component are obtained by analyzing a mixture of said minorcomponent with either said major component or a representative mixtureof the target product.
 6. Method according to claim 1 characterized inthat the number of “n” components of the target product is greater thanor equal to three.
 7. Method according to claim 1 characterized in thatit performs an additional step following the second step of claim 1,said additional step consisting of the constitution from the spectraldata characterizing the components (preferably from the spectraldatabases characterizing the components) of new spectral datacharacterizing the mixtures of said components (preferably new spectraldatabases characterizing the mixtures of said components), and thenusing these new spectral data (preferably these new spectral databases)in the third step of claim
 1. 8. Method according to claim 1characterized in that the target product is a petroleum product, forexample a fuel, for example a gas oil and/or a gasoline.
 9. Methodaccording to claim 1 characterized in that the preparation is carriedout in a refinery, a (petro-) chemical complex, a petroleum depot and/orany plant using a batch and/or in-line mixture of components, inparticular in a terminal or any post-refinery fuel mixing facility. 10.Use of a method according to claim 1, for preparing a target productcomprising a component “X”, comprising favoring the use of saidcomponent “X” from among the “n” components of the target productmixture, the preparation method including an additional step consistingin selecting from the ranges of respective proportions of the componentsbeing fed into the mixer of step 3 of claim 1, the proportion rangehaving the highest concentration of “X” component.
 11. Use of a methodaccording to claim 1 a. to validate the replacement of component “X” bycomponent “X′”, and/or b. predicting the potential use rate of component“X′” during preparation of a target product initially comprisingcomponent “X”, the preparation process including a. a spectral analysisstep of component “X′” to determine a characterizing spectral datum, forexample a spectrum and/or a characterizing spectral databank, b. a stepconsisting of a step to replace the spectral datum characterizingcomponent “X” with the spectral datum characterizing component “X′” inthe Process, c. a validation step to replace an “X” component with an“X′” component and/or to predict the potential rate of use of thecomponent “X′” for the third step of claim 1 that makes it possible toverify that at least one range exists of the respective proportions ofthe new components necessary to the reconstitution of a spectral datumof the mixture belonging to the spectral domain of the target product.